The article provides a shared computation-based view on concepts in AI, cognitive science and neuroscience. Advances that address challenges of perception and action under uncertainty are discussed.(more…)

This paper deals with the problem of model-based reinforcement learning (RL) from images. The idea behind model-based RL is to learn a model of the transition dynamics of the system/robot and use this model as a surrogate simulator. This is helpful if we want to minimize experiments with a (physical/mechanical) system. The added difficulty addressed in this paper is that this predictive transition model should be learned from raw images where only pixel information is available.

The objective of this paper is to predict links in social networks. The working assumption is that links depend on relational features between entities. The objective of the paper is to simultaneously infer the number of these features and learn which entities have each feature.(more…)

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arXiv has become a main source of information for statistics and machine learning. Daily email digests tell me what papers have been uploaded since yesterday, including authors, abstracts and a link to the paper. For me this is invaluable at the receiving side.

However, on the producing/publishing side, not everybody thinks that uploading papers to arXiv is a good idea. And there are several good reasons for this:(more…)

This paper was clearly one of my highlights at ICML and falls into the category of large-scale kernel machines, one of the trends at ICML. Wilson and Nickisch combine the advantages of inducing point and structure-exploiting (e.g., Kronecker/Toeplitz) approaches.

In this paper, the authors propose to map data to a low-dimensional Euclidean space, such that the inner product in this space is a close approximation of the inner product computed by a stationary (shift-invariant) kernel (in a potentially infinite-dimensional RKHS). The approach is based on Bochner’s theorem.

The paper is about large-scale Gaussian process classification. Unlike many others, the authors use Expectation Propagation (and not Variational Inference) for approximate inference. An approximate marginal likelihood expression is derived that factorizes over the data instances, which allows for distributed inference and training. Training is additionally sped up by using mini-batches of data.